In this preliminary section, we’ll cover basic information that will help you to get started with RStudio.
If you haven’t already, please go ahead and install both the R and RStudio applications. R and RStudio must be installed separately; you should install R first, and then RStudio. The R application is a bare-bones computing environment that supports statistical computing using the R programming language; RStudio is a visually appealing, feature-rich, and user-friendly interface that allows users to interact with this environment in an intuitive way. Once you have both applications installed, you don’t need to open up R and RStudio separately; you only need to open and interact with RStudio (which will run R in the background).
The following subsections provide instructions on installing R and RStudio for the macOS and Windows operating systems. These instructions are taken from the “Setup” section of the Data Carpentry Course entitled R for Social Scientists. The Data Carpentry page also contains installation instructions for the Linux operating system; if you’re a Linux user, please refer to that page for instructions.
The Appendix to Garret Grolemund’s book Hands on Programming with R also provides an excellent overview of the R and RStudio installation process.
.exe file that was just downloaded..pkg file for the latest R version.Now that we’ve installed and opened up RStudio, let’s familiarize ourselves with the RStudio interface. When we open up RStudio, we’ll see a window that looks something like this:
The RStudio Interface
If your interface doesn’t look exactly like this, it shouldn’t be a problem; we would expect to see minor cosmetic differences in the appearance of the interface across operating systems and computers (based on how they’re configured). However, you should see four distinct windows within the larger RStudio interface:
File button on the RStudio menu bar, scroll down to New File button, and then select R Script from the menu bar that opens up.View() function, which will display the relevant data within a new tab in the Source window.R is an open-source programming language for statistical computing that allows users to carry out a wide range of data analysis and visualization tasks (among other things). One of the big advantages of using R is that it has a very large user community among social scientists, statisticians, and digital humanists, who frequently publish R packages. One might think of packages as workbooks of sorts, which contain a well-integrated set of R functions, scripts, data, and documentation; these “workbooks” are designed to facilitate certain tasks or implement useful procedures. These packages are then shared with the broader R user community, and at this point, anyone who needs to accomplish the tasks to which the package addresses itself can use the package in the context of their own projects. The ability to use published packages considerably simplifies the work of applied data research using R; it means that we rarely have to write code entirely from scratch, and can build on the code that others have published in the form of packages. This allows applied researchers to focus on substantive problems, without having to get too bogged down in complicated programming tasks.
In this workshop, we will use the following packages to carry out relevant data analysis and visualization tasks (please click the relevant link to learn more about a given package; note that the tidyverse is not a single package, but rather an entire suite of packages used for common data science and analysis tasks): + tidyverse: + wosr
To install a package in R, we can use the install.packages() function. A function is essentially a programming construct that takes a specified input, runs this input (called an “argument”) through a set of procedures, and returns an output. In the code block below, the name of the package we want to install (here, the tidyverse suite) is enclosed within quotation marks and placed within parentheses after printing install.packages Running the code below will effectively download the tidyverse suite of packages to our computer:
# Installs "tm" package
install.packages("tidyverse")
To run this code in your own R session:
Edit menu of your browser).Below, we can see how that line of code should look in your script, and how to run it:
Installing tidyverse in R Script
Please note that you can follow along with the tutorial on your own computers by transferring all of the subsequent codeblocks into your script in just this way. Run each codeblock in your RStudio environment as you go, and you should be able to replicate the entire tutorial on your computer. You can copy-paste the workshop code if you wish, but we recommend actually retyping the code into your script, since this will help you to more effectively familiarize yourself with the process of writing code in R.
Note that the codeblocks in the tutorial usually have a comment, prefaced by a hash (“#”). When writing code in R (or any other command-line interface) it is good practice to preface one’s code with brief comments that describe what a block of code is doing. Writing these comments can allow someone else (or your future self) to read and quickly understand the code more easily than otherwise might be the case. The hash before the comment effectively tells R that the subsequent text is a comment, and should be ignored when running a script. If one does not preface the comment with a hash, R wouldn’t know to ignore the comment, and would throw an error message.
Now, let’s install the other packages we mentioned above, using the same install.packages() function:
install.packages("wosr")
All of the packages we need are now installed!
However, while our packages are installed, they are not yet ready to use. Before we can use our packages, we must load them into our environment. We can think of the process of loading installed packages into a current R environment as analogous to opening up an application on your phone or computer after it has been installed (even after an application has been installed, you can’t use it until you open it!). To load (i.e. “open”) an R package, we pass the name of the package we want to load as an argument to the library() function. For example, if we want to load our tidyverse packages into the current environment, we can type:
# Loads tidyverse packages into memory
library(tidyverse)
At this point, the full suite of the tidyverse suite’s functionality is available for us to use.
Now, let’s go ahead and load the remainder of the packages that we’ll need:
# loads remainder of required packages
library(wosr)
At this point, the packages are loaded and ready to go! One important thing to note regarding the installation and loading of packages is that we only have to install packages once; after a package is installed, there is no need to subsequently reinstall it. However, we must load the packages we need (using the library function) every time we open a new R session. In other words, if we were to close RStudio at this point and open it up later, we would not need to install these packages again, but would need to load the packages again.
Before we can get a sense of how to work with data in R, it is important to familiarize ourselves with basic features of the R language’s syntax,
setting working directory